To make accurate predictions, Spatial-Temporal Graph Neural Networks (STGNNs) have attracted increasing attention recently.
STGNNs combine Graph Neural Networks (GNNs) [1]} and sequential models.
The former is used to deal with the dependencies between time series, and the latter is used to learn the temporal patterns.
Benefitting from jointly modeling the spatial and temporal patterns, STGNNs have achieved state-of-the-art performance.
In addition, an increasing number of recent works are further exploring the joint learning of graph structures and STGNNs since the dependency graph between time series, which is handcrafted by prior knowledge, is often biased and incorrect, even missing in many cases.
In short, spatial-temporal graph neural networks have made significant progress for multivariate time series forecasting in many real-world applications.
However, there is no free lunch.
More powerful models require more complex structures.
The computational complexity usually increases linearly or quadratically with the length of the input time series.
Further considering the number of time series (e.g., hundreds), it is not easy for STGNNs to scale to very long-term historical time series.
In fact, most models use historical data in a small window to make predictions, e.g., use the past twelve time steps (one hour) to predict the future twelve time steps [2]}, [3]}, [4]}, [5]}, [6]}.
The inability to explicitly learn from long-term information brings up some intuitive concerns.
